Public Health

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to expre...

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, a...

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research...

Comparative Study on the Efficacy of MF 59, ISA70 VG, and Nano-Aluminum Hydroxide Adjuvants, Alone and with Nano-Selenium on Humoral Immunity Induced by a Bivalent Newcastle+Avian Influenza Vaccine in Chickens.

Newcastle disease (ND) and Avian influenza (AI) are the major problems and the most economically important viral diseases in the poultry industry; the...

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning.

BACKGROUND: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology s...

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion.

Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, ...

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitab...

When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?

The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies ...

An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information.

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data sour...

Perplexing issues for convalescent immune plasma therapy in COVID-19.

Convalescent immune plasma (CIP) therapy in coronavirus disease 2019 (COVID-19) is presently a trendy choice of treatment. On March 24, 2020, the Unit...

Factors That Influence Risk Perceptions and Successful COVID-19 Vaccination Communication Campaigns With American Indians.

COVID-19 vaccinations are the primary tool to end the pandemic. However, vaccine hesitancy continues to be a barrier to herd-immunity in the United St...

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle a...

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in loca...

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomogra...

Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual...

Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological ...

A collaborative robotic solution to partly automate SARS-CoV-2 serological tests in small facilities.

The outbreak of COVID-19 has introduced a significant stress on the healthcare systems of many countries. The availability of quick and reliable scree...

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany.

Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart fr...

Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis.

The coronavirus disease 2019 (COVID-19) is an ongoing pandemic with high morbidity and mortality rates. Current epidemiological studies urge the need ...

Intelligent type 2 diabetes risk prediction from administrative claim data.

Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if...